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摘要翻译：
本文研究了线性面板数据模型的广义最小二乘(GLS)估计。通过一致估计大误差协方差矩阵，所提出的可行GLS(FGLS)估计器在存在异方差、序列和横截面相关时比普通最小二乘(OLS)更有效。为了考虑序列相关性，我们采用了条带法。为了考虑横截面相关性，我们建议使用阈值化方法。我们建立了所提出的估计量的极限分布。一项蒙特卡洛研究被考虑。将所提出的方法应用于一个经验应用。
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英文标题：
《Feasible Generalized Least Squares for Panel Data with Cross-sectional
and Serial Correlations》
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作者：
Jushan Bai, Sung Hoon Choi, Yuan Liao
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最新提交年份：
2020
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分类信息：
一级分类：Economics 经济学
二级分类：Econometrics 计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
This paper considers generalized least squares (GLS) estimation for linear panel data models. By estimating the large error covariance matrix consistently, the proposed feasible GLS (FGLS) estimator is more efficient than the ordinary least squares (OLS) in the presence of heteroskedasticity, serial, and cross-sectional correlations. To take into account the serial correlations, we employ the banding method. To take into account the cross-sectional correlations, we suggest to use the thresholding method. We establish the limiting distribution of the proposed estimator. A Monte Carlo study is considered. The proposed method is applied to an empirical application.
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PDF链接：
https://arxiv.org/pdf/1910.09004
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